Berliana Devianti Putri
Fakultas Kesehatan masyarakat, universitas Airlangga

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Peran Faktor Keluarga Dan Karakteristik Remaja Terhadap Perilaku Seksual Pranikah Putri, Berliana Devianti
Biometrika dan Kependudukan Vol 3, No 1 (2014): Jurnal Biometrika dan Kependudukan
Publisher : Biometrika dan Kependudukan

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ABSTRACT Adolescent reproductive health issues can hinder quality improvement adolescents, one of reproductive behavior is unhealthy pre-marital sexual behavior. This study was observational explanative with cross-sectional design. The goal was analyze the influence of the characteristics and the family factors on adolescent to pre-marital sexual behavior in high school students "X" Surabaya. The population was all students which studying in high school "X" Surabaya in 2014. Sampling was stratified random sampling and obtained 91 students. The independent variables were age, sex, allowance, parenting, family communication patterns, and family form. The analysis used multinomial multiple logistic regression, with the reference category was pre-marital sexual behavior of low risk and  the level of significance α = 5%. The result showed that pre-marital sexual was influenced by sex, parenting, family communication patterns, and family form (p<0,05).Keywords : adolescent, family factors, pre-marital sexual
Comparison of MICE and Regression Imputation for Handling Missing Data Putri, Berliana Devianti; Notobroto, Hari Basuki; Wibowo, Arief
Health Notions Vol 2 No 2 (2018): February 2018
Publisher : Humanistic Network for Science and Technology (Address: Cemara street 25, Ds/Kec Sukorejo, Ponorogo, East Java, Indonesia 63453)

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Data collection activities have a higher risk of missing data. Missing data may produce biased estimates and standard errors increased, so imputation method is needed. The purpose of this study was to investigate which imputation method is the most appropriate to use for handling missing data. The strategies evaluated include complete case analysis, Multivariate Imputation by Chained Equation (MICE), and Regression Imputation. This study was non-reactive study and used raw data RPJMN 2015 Survey from BKKBN East Java Province. There were three incomplete data sets were generated from a complete raw dataset with 5%, 10%, and 15% missing data. Incomplete data sets were made missing completely at random. Based on Friedman Test, both of imputation methods produced estimates which was no different with complete raw data set. Based on Mean Square Error analysis, MICE provided MSE values less and more stable than Regression Imputation in all scenarios. Conclusion: Multivariate Imputation by Chained Equation (MICE) was the most recommended method to use for handling missing data less than 15%.
Comparison of MICE and Regression Imputation for Handling Missing Data Putri, Berliana Devianti; Notobroto, Hari Basuki; Wibowo, Arief
Health Notions Vol 2, No 2 (2018): February
Publisher : Humanistic Network for Science and Technology (HNST)

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Abstract

Data collection activities have a higher risk of missing data. Missing data may produce biased estimates and standard errors increased, so imputation method is needed. The purpose of this study was to investigate which imputation method is the most appropriate to use for handling missing data. The strategies evaluated include complete case analysis, Multivariate Imputation by Chained Equation (MICE), and Regression Imputation. This study was non-reactive study and used raw data RPJMN 2015 Survey from BKKBN East Java Province. There were three incomplete data sets were generated from a complete raw dataset with 5%, 10%, and 15% missing data. Incomplete data sets were made missing completely at random. Based on Friedman Test, both of imputation methods produced estimates which was no different with complete raw data set. Based on Mean Square Error analysis, MICE provided MSE values less and more stable than Regression Imputation in all scenarios. Conclusion: Multivariate Imputation by Chained Equation (MICE) was the most recommended method to use for handling missing data less than 15%. Keywords: Missing data, MICE, Regression imputation